1. Field of the Invention
The invention relates to the development of computer-assisted diagnostic (CAD) methods for the analysis of digital X-ray images or gray-scale images generated by other digital sensors. More particularly, the invention relates to the use of CAD methods for the analysis of mammography images.
2. Description of Related Art
The use of computer-assisted diagnostic (CAD) methods has been proposed as a second opinion strategy for various medical imaging applications that include breast screening using digital mammography. The goals of the CAD methods are to improve mammographic sensitivity by increasing the detection of potentially significant suspicious areas, to improve specificity by reducing false-positive interpretations, and to classify suspicious areas as benign or malignant, ultimately reducing the number of unnecessary biopsies of benign lesions (Giger, 1993; Vyborny et al., 1994; Adler et al., 1995). Two major roles of CAD in mammography are detection and classification. These have been primarily directed towards the study of microcalcifications and masses, where the masses are comparatively more difficult to detect than microcalcifications because masses can be simulated or obscured by normal breast parenchyma.
Mass Detection Methods. Numerous investigators have addressed mass detection and classification. The methods reported in the literature for mass detection can be grouped into two categories. In the first category, the methods involve the use of asymmetry measures of the left and right breast. The detection of masses is based on deviations from the architectural symmetry of normal right and left breasts, with asymmetry indicating suspicious areas. The basic approach of the methods in this category is to search for brightness or texture differences between corresponding locations on left and right images (Winsberg et al., 1967; Hand et al., 1979; Semmlow et al., 1980; Kimme et al., 1975; Hoyuer et al., 1978/79; Lau et al., 1991).
Giger and her colleagues (Giger et al., 1990; Yin et al., 1991) expanded on this approach of using left-to-right breast asymmetries for the detection of subtle masses. Multiple subtraction images are formed to enhance asymmetries. Feature extraction is used to decrease the number of false-positive detections.
Miller and Astley (193) proposed an automatic method for detecting asymmetry based on the comparison of corresponding anatomic structures identified by the shape and brightness distribution of these regions. The detection performance of these methods depends strongly on two factors, the alignment of left and right breast images and the individual feature analysis of breast images. Due to the fact that the size and shape of the two breasts may be different, it may be difficult to identify accurately corresponding locations. The asymmetry cues generated may not be sufficiently specific to be used as prompts for small and subtle abnormalities in CAD systems. Individual image feature analysis is the basis of alignment and the comparison of symmetry, and is closely related to the second class of mass detection methods.
The second class of CAD methods focus on the determination of features that allow the differentiation of masses from normal parenchymal tissues in a given image. They consist of two major steps: feature extraction and discrimination. The features include textures derived from a gray-level dependence matrix (Miller and Astley, 1993; Chan et al., 1995), texture energy obtained from the output of Law's filters (Gupta et al., 1995), density and morphological features (Lai et al., 1989; Brzakovic et al., 1990). Recently, Chan et al. (Petrick et al., 1995) investigated the advantage of combining the morphological and texture features for mass detection. Single-scale preprocessing methods have also been reported for improved feature extraction such as histogram equalization, morphological operators (Gupta et al., 1995), and selective median filtering (Lai et al., 1989). Cerneaz and Brady (Cerneaz et al., 1994) presented a technique for extracting a description of the curvilinear structures in a form that allows many difficulties resulting from the complication of intense textural components of images in the analysis of a mammogram to be overcome. Alternatively, because the size, shape, and the gray-level profile of the masses vary from case to case in mammograms, multiresolution analysis methods have been used in mass detection for improved image segmentation, feature enhancement, and extraction based on fuzzy pyramid linking (Brzakovic et al., 1990) and Gaussian filters (Barman et al., 1993, 1994).
Wavelet-based methods have been proposed for image enhancement in digital mammography. For example, Laine et al. (1994) used wavelet maxima coefficients for image enhancement and multiscale edge representation, the work being a modification of the dyadic wavelet transform originally proposed by Mallat (1992). Various approaches were used for image enhancement, including the use of linear, exponential, and constant weight functions for modification of the coefficients of the original dyadic wavelet transform. In this work the use of preprocessing for noise removal was not used, and so multiscale edges are mixed with structured noise in digital mammograms. Similarly, a modification of the coefficients of the dyadic wavelet transform may result in less than optimal reconstruction. Similarly, the wavelet transforms are not implemented on filter banks such as quadrature mirror filters. Emphasis is placed on the use of the dyadic wavelet transform for image enhancement for improved visual diagnosis.
Malignant breast lesions are frequently characterized by a stellate or spiculated appearance n x-ray mammograms. The automated detection of such lesions is a challenging task because of the high degree of similarity between such lesions and other normal structures within the breast. CAD methods have also been reported with a specific emphasis on speculated lesions. The methods include: (a) analysis of the orientation of edges through the image to identify areas of locally radiating structure, with false positive reduction using Law's texture analysis (Kegelmeyer, 1992; Kegelmeyer et al., 1994; (b) use of radial line enhancement followed by Bayesian combination of caes generated by the Hough transform (Astley, et al., 1993); (c) use of gray-scale seed-growing methods to allow analysis of radial gradient histograms surrounding the mass of interest (Giaer et al., 1994a), and finally (d) detection of stellate patterns without assuming the presence of a central mass for the detection of subtle cancers with line orientation obtained by three second order Gaussiar derivative operators (Karssemeijer, 1994).
Based on the principle of image formation and the human visual system (HVS) perception analysis of blur and edge localization, Claridge and Richter (Clariage et al., 1994) investigated methods of improving the diagnosis of mammographic lesions by using computer image analysis methods for characterization of lesion edge definition and accurate localization of the lesion boundary.
Classification methods proposed to differentiate masses from normal tissue have included the use of decision trees (Li et al., 1995; Kegelmeyer et al., 1994), Bayes classifiers (Brzakovic et al., 1990), linear discriminant analysis (Chan et al., 1995), linear and quadratic classifiers (Woods et al., 1994), and neural networks (NN) (Petrick et al., 1995; Wu et al., 1993).
The methods known in the art generally use sensitivity of detection and false positive detection rate as means for evaluating CAD algorithms (Lai et al., 1989; Brzakovic et al., 1990) and have met with varying levels of success for the databases containing only circumscribed, speculated, or all types of masses. Since the image databases vary, a direct comparison cannot be made.
Mass Classification (Benign versus Malignant). Features proposed for mass classification specifically using visual criteria have been reported (Wu et al., 1993) that closely correlate with the American College of Radiology (ACR) Lexicon visual criteria. They include density-related features, shape and size features, mass margins, spiculations, and correlation with other clinical data, including the patient's age. The computation of image-related features has been proposed (Kilday et al., 1993) with an emphasis on gross and fine shape-related features that include three radial length measures, tumor boundary roughness, and area parameters. The use of mass-intensity-related features using fractional dimension analysis and nonlinear filters has been proposed for quantifying the degree of lesion perfusion to identify malignant lesions with rough intensity surfaces (Burdett et al., 1993).
Since most breast carcinomas have the mammographic appearance of a stellate lesion, spiculation analysis has been greatly emphasized. Known methods include: (a) use of a line-enhancement operator to measure linearity, length, and width parameters (Parr et al., 1994), (b) second-order Gaussian derivatives to measure line orientation to determine the total number of pixels pointing in the direction of the center of the mass, with a binomial statistical analysis of the angular distributions of the spiculations (Karssemeijer, 1994). Claridge et al. (1994) used a similar spiculation index to reflect the relative magnitude of horizontal/vertical directions. (c) Huo et al. (1995) have reported a comprehensive approach using both radial edge gradient analysis and cumulative edge gradient distribution, determined by seed growing, within manually defined regions of interest.
Several investigators have indicated the need to include features in all three domains (gray scale, morphological, and texture) to improve classification performance (Huo et al., 1995; Parr et al., 1994). Similarly, feature extraction of speculations may be influenced by local variations in parenchymal tissue background (Huo et al., 1995; Parr et al., 1994). However, spiculation analysis may provide a means of differentiation of spiculations from other directional features within the mammogram (Karssemeijer, 1994). The classificaticon methods employed are similar to those used for mass detection, with an emphasis on the use of back-propagation NNs (Huo et al., 1995; Wu et al., 1993), where variations in She output node of the NN can be employed to generate computer or simulated receiver operating characteristic (ROC) curves as a means for evaluation of the CAD method or modification to CAD modules (Huo et al., 1995; Lo et al., 1995). The accuracy of a detection or classification algorithm can be characterized entirely by an ROC curve (Metz, 1986, 1989) or a free-response ROC (FROC) curve (Chakraborty, 1989).